How to train your own big model
Training a large model involved many aspects, and the following were some of the key elements:
** 1. Model structure **
1. ** Single-round dialogue sample **
- In the big model training, questions and instructions could be used as prompt input and answers as output. When calculating loss, the pad token had to be blocked.
2. ** Multiple dialogue samples **
- One way was to assume that the multiple rounds were Q1A1/Q2A2/Q3A3, which could be converted into three training samples: Q1->A1, Q1A1Q2- >A2, and Q1A1Q2A2Q3- >A3. However, there was a problem with this method. Most of the data was a pad token, which led to inefficient utilization of training data. There would also be the problem of data repetition. The repeated expansion of training data was the number of sessions * average number of rounds. At the same time, the repetition of the previous part would also make the training efficiency low.
- The improved method was for decoder-only models. The input of the multi-round dialogue sample was
How to train a model
Based on context alone Training a model is a complicated process. The following are some basic steps: ** 1. Data Collection and Pre-processing ** 1. ** Data Collection ** - For example, if the model was used for image recognition, a large amount of image data would need to be collected; if the model was used for natural language processing, text data would need to be collected. These data sources could be public data sets, data obtained by web crawlers (subject to relevant laws and regulations), internal data of enterprises, and so on. 2. ** Data Cleansing ** - For example, in text data, there may be some garbled characters, incomplete sentences, etc., and in image data, there may be damaged image files. - To deal with missing values, methods such as mean and mean filling could be used for numerical data, but special processing logic might be needed for text data. 3. ** Data annotation (if applicable)** - For supervised learning models, such as classification models, the data needed to be labeled. For example, in a sentiment analysis task, one had to label the text with positive or negative emotions. ** 2. Choose a suitable model architecture ** 1. ** Choose based on mission type ** - For sequence data processing tasks such as machine translation and speech recognition, the Cyclic Neutral Network (RHN) and its variants (such as LSTMs, Gru) or the Transformer architecture may be more suitable. - For image recognition tasks, Consecutive neural networks (CCNs) were a common choice. - For general prediction tasks, multi-layer perceptrons (MLPs) could be considered. 2. ** Consider model complexity ** - An overly simple model might not fit the data well, resulting in an underfit, while an overly complex model might overfit the data and perform poorly on new data. You can choose the number of layers and the number of neurons based on the size and complexity of the data. ** 3. define the loss function and optimiser ** 1. ** Loss Function ** - It measured the difference between the model's prediction and the actual result. For example, in the return task, the mean square error (MSe) was often used as the loss function, and in the classification task, the cross-entropies loss function was more common. 2. ** Optimizer ** - The optimiser is used to adjust the parameters of the model to minimize the loss function. The most common optimisers are Stochastical Slope Descent (STD) and its variants (Adaglad, Adadelta, Adam, etc.). ** 4. Model training ** 1. ** Divide Data Sets ** - The data set was usually divided into training set, verification set, and test set. The training set was used to train the model, the verification set was used to adjust the hyperparameters of the model during the training process (such as learning rate, layer number, etc.), and the test set was used to finally evaluate the performance of the model. 2. ** Initialize parameters ** - Initialize the parameters of the model. The common methods for this were random initializations, Xavier initializations, and so on. 3. ** Start training ** - During the training process, the model was used to perform forward transmission based on the input data to obtain the prediction results. Then, the loss function was calculated, and the parameters of the model were updated through backward transmission through the optimiser. This process would be iterated many times until the stopping condition was met (such as reaching the specified number of iterations, the loss on the verification set no longer decreasing, etc.). ** 5. Model evaluation and optimization ** 1. ** Evaluation Index ** - According to the type of mission, choose the appropriate evaluation index. For example, in a classification task, accuracy, recall, F1 value, etc. could be used; in a return task, mean absolute error (MAE) could be used. 2. ** Hyper-parameters adjustment ** - If the model did not perform well on the verification set, you could adjust the hyperparameters of the model, such as changing the learning rate, increasing or decreasing the number of layers, and then retrain the model. 3. ** Model Fusion (option)** - Multiple different models or different versions of the same model could be merged to improve the performance of the model.
How to Train a Network Model
The following are the general steps for training a network model: 1. ** Create/import data set **: - "import official data set: The official data set is stored in torchvision.datasets." - To import your own data set, you need to rewrite the datasets and dataloader sections and annotate the data set. Generally, three data sets were needed for model training, namely the training set, the test set, and the verification set. The data ratio was about 1:1:8. The training set was used to train the model, the verification set was used to adjust the parameters and hyperparameters of the model to improve the accuracy of the model, and the test set was used to test the model to verify the detection effect of the model. 2. ** Data set **: You can only use the dataloader method to transfer the data set into the neural network for model training. 3. ** Building network model and instantiating it **: You can refer to the related knowledge of neural network skeleton building and convolutions, neural network-Consecutive layer and Pooling layer, neural network-ReLV and linear layer. 4. ** define loss function and optimiser **: refer to the introduction of loss function and optimiser. 5. ** Setting the network model training parameters **: When the training set is input into the training, a loss function needs to be set to test the difference between the output score and the target value. The error is back-propagated to calculate the slope, and the slope is used to improve the model parameters. 6. ** Model Validation **: Generally, after a round of model training, the optimized model will be verified using the verification set. During this process, only the model will be used and its relevant parameters will not be adjusted. You can use the torch.no_grad() method to retain the model's grads and test them with relevant parameters such as error rate and accuracy rate. 7. ** Training curve visualization **: Generally, it is difficult to directly display the specific performance of the model only by data display. Therefore, it is necessary to visualize the relevant parameters in the verification method, such as loss rate, accuracy rate, recall rate, etc., which can be visualized using matplotLib and tensorboard. 8. ** Save model parameters **: Save the model with the best performance during training. You can use torch.save() to save the entire model. If you only want to save the model parameters, you can use the state_dict() method to get the model's parameters dictionary and then save it. If different devices (such as CPU and CPU) were used to save and load the model parameters, the loading would fail. Therefore, when saving the model, you should ensure that the model and parameters are on the same device, and specify the same device when loading.
Pangu AI big model
The Pangu model was an artificial intelligence model jointly developed by Huawei Cloud, Circulation Intelligence, and Pengcheng Laboratory. It included the NMP (Natural Language Processing) model, the MV (Machine Vision) model, and the scientific computing model. It was officially released in April 2021. The Pangu Model 5.0, released on June 21st, 2024, was upgraded in three aspects: full series, multi-mode, and strong thinking. In terms of multi-modes, it could better and more accurately understand the physical world, including text, pictures, video, radar, infrared, remote sensing, and more. For example, identifying satellite remote sensing images to analyze regional crop growth for yield estimation, pest and disease monitoring, and so on. Models with different parameters can be adapted to different business scenarios. For example, the Pangu E series with billions of parameters supports end-side intelligent applications such as mobile phones and PCs; the Pangu P series with billions of parameters is suitable for low-delay and high-efficiency reasoning scenarios; the Pangu U series with billions of parameters handles complex tasks; and the Pangu S series super large model with trillions of parameters helps enterprises handle complex cross-domain multi-tasks. The Pangu model had many advantages and wide applications. Compared to ChatGPM, Pangu not only supported text, picture, and video input, but also supported radar, infrared, and remote sensing, which could simulate the real physical world. For example, in the aspect of intelligent driving, there is no need for modeling. It can carry out physical reasoning, automatic adjustment of parameters, detection and identification of various objects, realize intelligent driving without relying on high-definition maps, and can be applied to other vehicles through manual driving learning. It has applications in high-speed rail inspection, national grid power grid inspection, Wugang, Shanghai Development Bank, government affairs and other fields. In terms of weather, the Pangu Meteorology Model was the first AI model with accuracy exceeding traditional numerical prediction methods. Its speed was more than 10,000 times faster than traditional numerical prediction methods. It could provide global weather forecast in seconds. Its weather forecast results included a variety of weather elements and could be directly applied to multiple weather research sub-scenarios. The European Medium-Range Weather Projection Center had launched the model for free viewing of the weather forecast for the next 10 days. In addition, the Pangu model could allow the robot to complete more than ten complex mission plans, generate virtual videos needed by the robot to learn complex scenes, realize object recognition, question and answer interaction, high-fives and water delivery, and other functions in the field of humanoid robots. It could also enable various forms of industrial robots and service robots to do dangerous and heavy work. The Huawei Cloud Pangu NMP model passed all 38 tasks such as text analysis, abstract summary, text rewrite, and knowledge Q & A, demonstrating comprehension ability (such as text analysis, reasoning, etc.) and generation ability (such as abstract summary, machine translation, etc.). Its overall model architecture is layered, including basic and high-level abilities in terms of service core technical capabilities, and can be improved in various ways to provide humane Q & A services in the field of government affairs and improve office efficiency. 'The Myth of True Love in the Pangu Progenitor Universe' is equally wonderful. Please click to read it!
Hunyuan AI big model app
There were applications related to the Hunyuan model, such as the Hunyuan Assistant Mini programs (already online on WeChat), and the Hunyuan model APP (which could be used to operate 3D characters such as DreamWorks). In addition, the company also released the Yuanbao APP based on the Hunyuan model technology, which connected to more than 600 business scenarios of the company.
AI Big Model Ranking List
On June 28th, 2024, in the AI open source model ranking list released by Hugging Face, Ali Tongyi's Thousand Questions Model Qwen2 - 72B once again reached the top, becoming the No.1 in the global open source model ranking list. The second place was Facebook's llama3. Moreover, Ali's previous open source Qwen 1.5 basic and Chat versions were also on the list. In the top 10 of this list, Ali occupied four places. In addition, there was the November ranking of China's AI big model platform, but the reference materials did not give the specific ranking. 'The Myth of True Love in the Pangu Progenitor Universe' is equally wonderful. Please click to read it!
AI big model for writing papers
Here are some big AI models that might be helpful in writing a thesis: - ** GT4 **: Has powerful natural language processing capabilities, can help generate the framework of the thesis, provide relevant opinions and arguments, etc. However, it was a foreign model, and there might be network and data security restrictions when using it. - ** A Word from the Heart **: Able to understand user needs and provide ideas and materials for thesis writing. It was an artificial intelligence developed by Baidu and could use Baidu's knowledge graph and other resources. - ** Iflystar **: Can be used to analyze the topic of the thesis, providing some support in terms of language expression and logical combing. When writing a thesis using the AI model, you can use the following functions: - ** Generation of content **: Generates relevant paragraph content according to the theme entered. However, you need to check the accuracy and creativity of the generated content to avoid plagiarism. - ** Thought Expansion **: When encountering a bottleneck in thinking, the large model can provide some different perspectives and ideas to help broaden the vision of the thesis. - ** grammar and format checking **: Some large models can check for grammar errors in the thesis and give suggestions for format. However, when writing a thesis using an AI model, one should also pay attention to: - ** Accuracy verification **: You can't completely rely on the output of the model. You need to carefully verify the facts and data. - ** Academic standards **: Follow academic ethics and standards, use AI tools reasonably, and ensure that the thesis is a reflection of your own research results. " A Short History of the Future: Legends of the Intelligent Era " was equally exciting. Everyone was welcome to click and read it!
How can a model create their own success story?
A model can create their own success story by first having a unique look. For example, if a model has a distinct feature like Lily Aldridge's curly hair, it can make them memorable. Second, they need to be professional. This means being on time for castings and shoots, and following the instructions of the photographers and designers. Third, networking is important. By making connections in the fashion industry, like getting to know fashion editors or other models, they can open up more opportunities.
Huawei AI big model concept stock
Concepts related to the Huawei AI model include: Leo shares (Active in the market in cooperation with the big language model), Jincai Internet (Cooperating with Huawei to develop the "Xinzhi Yue Finance Model"), Beixinyuan (Source Mixin AI ability platform has been adapted to domestic excellent large model AI products), Dongfang Guoxin, Rongji Software, Duolun Technology, Haoyun Technology, Gao Xinxing, Changliang Technology, Human M Network, Gaoling Information, Yuncong Technology, Sichuan University Zhisheng, Cape Cloud, Xinchen Technology, Tuowei Information, Changshan Beiming, Tongfang, Digital China (These companies have completed compatibility testing or in-depth cooperation with Huawei's MindSpore), Jebsen (signed a video model cooperation with Huawei Cloud), Changshan Beiming (as a strategic partner of Huawei and other identities, the stock price was stimulated by Pangu's big model news), etc. 'The Myth of True Love in the Pangu Progenitor Universe' is equally wonderful. Please click to read it!
Pangu AI big model concept stock
Huawei Pangu Concept stocks are mainly distributed in IT services, vertical application software, black appliances, film and television theaters, special equipment, software development and other industries. The specific stock code is as follows: 1. IT services: iSoft Power (SZ301236), Yunding Technology (SZ000409), Beyondtech (SZ002649), Saiyi Information (SZ300687), Nanwei Software (SH603636), Dianke Digital (SH600850), Advanced Data Communication (SZ300541), Changshan Beiming (SZ000158); 2. vertical application software: Keda Controllers (BZ831832); 3. Black appliances: Jiulian Technology (SH688609); 4. Film and television theater: Jebsen (SZ300182); 5. Special equipment: Mayanson (SZ300275); 6. Software development: Tuoersi (SZ300229), Tuowei Information (SZ002261), Beixinyuan (SZ300352), Pan-micro Network (SH603039), and creative information (SZ300366). Please note that the stocks involved are not recommended and are sold at your own risk. 'The Myth of True Love in the Pangu Progenitor Universe' is equally wonderful. Please click to read it!
The leading company of the big model listed on the market
According to the information provided, some of the companies that had outstanding performance in the field of AI models were 360, iFlytek, Palm Reading Technology, Hengsheng Technologies, Hehe Information, etc. These companies had their own highlights in terms of AI layout and business development. For example, 360 fully embraced artificial intelligence, self-developed 360 GPM, and promoted the implementation of the "360 smart brain"; iFlyTe had related achievements such as iFlyTe Spark; Palm Reading Technology had a certain connection with Wenxin Yiyan; Hang Sen Electronic released the financial industry model LightGPM; He He Information released the acge text Vectorization Model, but did not explicitly mention which were listed leading companies. Sorry, based on the information I retrieved, I don't know how to accurately answer which are the leading companies in the AI model market. 'The Myth of True Love in the Pangu Progenitor Universe' is equally wonderful. Please click to read it!